1. Determinants of income inequality in the selected group of European countries : a panel data analysisDarja Boršič, Simon Matej Podgoršek, 2024, original scientific article Abstract: The paper focuses on providing basic characteristics of income inequality in a group of selected European countries in the period from 2000 to 2019. After presenting stylized facts and brief literature review, the paper proceeds to empirical analysis of income inequality in the observed countries by panel data techniques. Fixed and random effects models are estimated. After Hausman test approved the usage of fixed effects model, it was tested for serial correlation and robust standard errors were calculated. The empirical analysis of the determinants of income inequality shows that GDP growth, share of population in upper 10% of income percentile, Human Development Index and unemployment rate increase income inequality measured by Gini index, while share of workforce and share of population with tertiary education decrease income inequality. The results of the empirical analysis provide insight into determinants of income inequality, which may be useful for economic policy decision makers in their efforts to decrease the income inequality.
Keywords: income inequality, Gini index, economic growth, panel analysis, fixed effects model
Published in DKUM: 30.06.2025; Views: 0; Downloads: 8
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2. Pull factors and capital inflows : empirical insights from transformative dynamics in Southeast EuropeMehmed Ganić, Nedim Gavranović, 2024, original scientific article Abstract: This study seeks to examine pull factors of capital inflows, offering an
empirical analysis based on a panel study of eleven Southeast
European countries (Albania, Bosnia and Herzegovina, Bulgaria,
Croatia, Greece, Montenegro, North Macedonia, Kosovo, Romania,
Serbia, and Türkiye) over the period of 2004 – 2021. Methodologically,
the study utilizes a fixed effects (FE) regression model with robust
Driscoll-Kraay standard errors to address issues of heteroskedasticity,
autocorrelation, and potential cross-country correlation. The study
finds that several pull factors can be relevant in driving capital inflows
as follows: market size, inflation, financial and trade openness. The
empirical analysis confirms that the forces of trade liberalization,
financial liberalization, market size, real interest rates and inflation
stability are the elements that encourage capital inflows. On the other
hand, the estimated effects of current account balance and real
economic growth are not very convincing. Finally, we stress that more
study is required to fully understand the pull variables' ultimate
macroeconomic implications at the national level. The overall
influence of these positive (or negative) inflows may be moderated by
several characteristics, even if certain countries may be extremely
susceptible to these factors.
Keywords: capital inflows, pull factors, southeast Europe, panel data analysis, Driscoll-Kraay standard errors
Published in DKUM: 28.05.2025; Views: 0; Downloads: 2
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3. Multi-purpose use and lifecycle analysis of solar panelsDušan Strušnik, Urška Novosel, Jurij Avsec, 2020, original scientific article Abstract: The combined use of renewable energy technologies and alternative energy technologies is a promising approach to reduce global warming effects throughout the world. In this paper, the solar panel is used in combination with a heat pump or with biomass sources to obtain heat, electricity, and hydrogen. Based on the Rankine thermodynamic cycle, hydrogen could be obtained from water with electrolysis and the CuCl thermochemical cycle. Furthermore, this study contains a life cycle analysis of solar panels.
Keywords: heat pump, life cycle analysis, Rankine cycle, solar panel, thermochemical cycle
Published in DKUM: 01.12.2023; Views: 393; Downloads: 108
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4. Productivity and economic growth in the European Union : impact of investment in research and developmentAndreja Nekrep, Sebastjan Strašek, Darja Boršič, 2018, original scientific article Abstract: This paper focuses on investment in research and development as a factor of labour productivity and economic growth. Our analysis confirms the link between expenditure for research and development (expressed in % of GDP) and labour productivity (expressed in the number of hours worked) based on selected data for EU Member States in the period 1995-2013. A causal link between variables of the concave parabola was confirmed, and the value of expenditure for research and development (2.85% of EU GDP) maximising productivity (per hour of work) was determined based on the examined data. In accordance with these findings, EU’s target of reaching 3% of GDP spent on research and development to be achieved by 2020 seems in support of reaching maximum productivity in the EU.
Keywords: investment in research and development, productivity, economic growth, correlation, panel analysis
Published in DKUM: 03.05.2018; Views: 1991; Downloads: 253
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